Benchmarking Robustness and Generalization in Multi-Agent Systems: A Case Study on Neural MMO

no code implementations • 30 Aug 2023 • Yangkun Chen, Joseph Suarez, Junjie Zhang, Chenghui Yu, Bo Wu, HanMo Chen, Hengman Zhu, Rui Du, Shanliang Qian, Shuai Liu, Weijun Hong, Jinke He, Yibing Zhang, Liang Zhao, Clare Zhu, Julian Togelius, Sharada Mohanty, Jiaxin Chen, Xiu Li, Xiaolong Zhu, Phillip Isola

We present the results of the second Neural MMO challenge, hosted at IJCAI 2022, which received 1600+ submissions.

Benchmarking Reinforcement Learning (RL)

What model does MuZero learn?

no code implementations • 1 Jun 2023 • Jinke He, Thomas M. Moerland, Frans A. Oliehoek

Model-based reinforcement learning has drawn considerable interest in recent years, given its promise to improve sample efficiency.

Model-based Reinforcement Learning reinforcement-learning

RangL: A Reinforcement Learning Competition Platform

no code implementations • 28 Jul 2022 • Viktor Zobernig, Richard A. Saldanha, Jinke He, Erica van der Sar, Jasper van Doorn, Jia-Chen Hua, Lachlan R. Mason, Aleksander Czechowski, Drago Indjic, Tomasz Kosmala, Alessandro Zocca, Sandjai Bhulai, Jorge Montalvo Arvizu, Claude Klöckl, John Moriarty

The RangL project hosted by The Alan Turing Institute aims to encourage the wider uptake of reinforcement learning by supporting competitions relating to real-world dynamic decision problems.

OpenAI Gym reinforcement-learning +1

Distributed Influence-Augmented Local Simulators for Parallel MARL in Large Networked Systems

1 code implementation • 1 Jul 2022 • Miguel Suau, Jinke He, Mustafa Mert Çelikok, Matthijs T. J. Spaan, Frans A. Oliehoek

Due to its high sample complexity, simulation is, as of today, critical for the successful application of reinforcement learning.

Influence-Augmented Local Simulators: A Scalable Solution for Fast Deep RL in Large Networked Systems

no code implementations • 3 Feb 2022 • Miguel Suau, Jinke He, Matthijs T. J. Spaan, Frans A. Oliehoek

Learning effective policies for real-world problems is still an open challenge for the field of reinforcement learning (RL).

Reinforcement Learning (RL)

Online Planning in POMDPs with Self-Improving Simulators

1 code implementation • 27 Jan 2022 • Jinke He, Miguel Suau, Hendrik Baier, Michael Kaisers, Frans A. Oliehoek

To plan reliably and efficiently while the approximate simulator is learning, we develop a method that adaptively decides which simulator to use for every simulation, based on a statistic that measures the accuracy of the approximate simulator.

Influence-Augmented Online Planning for Complex Environments

1 code implementation • NeurIPS 2020 • Jinke He, Miguel Suau, Frans A. Oliehoek

In this work, we propose influence-augmented online planning, a principled method to transform a factored simulator of the entire environment into a local simulator that samples only the state variables that are most relevant to the observation and reward of the planning agent and captures the incoming influence from the rest of the environment using machine learning methods.

Influence-aware Memory Architectures for Deep Reinforcement Learning

1 code implementation • 18 Nov 2019 • Miguel Suau, Jinke He, Elena Congeduti, Rolf A. N. Starre, Aleksander Czechowski, Frans A. Oliehoek

Due to its perceptual limitations, an agent may have too little information about the state of the environment to act optimally.

reinforcement-learning Reinforcement Learning (RL)

Multitask Soft Option Learning

1 code implementation • 1 Apr 2019 • Maximilian Igl, Andrew Gambardella, Jinke He, Nantas Nardelli, N. Siddharth, Wendelin Böhmer, Shimon Whiteson

We present Multitask Soft Option Learning(MSOL), a hierarchical multitask framework based on Planning as Inference.

Transfer Learning